random-forest-regression

A program to predict prices of houses by taking into account the factors such as number of bedrooms, bathrooms, sqft of floor etc

Prediction of bikes demand using machine learning regression random forest algorithm

Insurance Premium Prediction

Capstone Project Module 3 - Noor Kharismawan Akbar

Machine learning project that predicts a mental health score out of 10 using lifestyle and behavioral factors, with a FastAPI backend and simple frontend UI.

End-to-end Machine Learning project that uses regression model to predict mental health scores using student demographic, academic, lifestyle, stress, and digital behavior data, with a FastAPI backend and web-based frontend.

Predict Air Grade

Price estimating of used bikes using historical Ebay prices

Used Random Forest, Decision Tree, Logistic Regression, and Supervised ML Algorithm

Explore the complete lifecycle of a machine learning project focused on regression. This repository covers data acquisition, preprocessing, and training with Linear Regression, Decision Tree Regression, and Random Forest Regression models. Evaluate and compare models using R2 score. Ideal for learning and implementing regression use cases.

Forecasting housing prices using regression models

Some videos have more impact than the others resulting in higher memorability scores for such videos. Using various ML algorithms, such memorability scores are predicted.

Prediction of gene expression from a given set of epigenomic features

Implementation of the Automatic Recognition with VAS Index (pain index) with the aim of demonstrating the effectiveness of the Random Forest on the problem.

🇵🇱🏠 The project predicts an apartment price for Warsaw, Krakow and Poznan. Distributed apartments by districts using geopandas; built XGBoost model with MAPE = 9% (the best of others).

This machine learning project focused on predicting food delivery times. The code emphasizes essential tasks such as data cleaning, feature engineering, categorical feature encoding, data splitting, and standardization to establish a solid foundation for building a robust predictive model.

This project is an IoT-based weather monitoring and prediction system that collects real-time temperature and humidity data using a NodeMCU (ESP8266) and a DHT11 sensor, logs it to a CSV file via a Python script, and uses machine learning(RandomForestRegressor) to predict future weather conditions.

A taxi company called Sweet Lift has collected historical data on taxi orders at the airport and they need to predict the number of taxi orders for the next hour.

End-to-end film box office analysis on 1M-row IMDB/TMDB data — cleaning, feature engineering, EDA (Exploratory Data Analysis), and Random Forest models comparing revenue drivers in the 1990s vs 2010s.

Random Forest Regression model for predicting football players market value based on FIFA player attributes.